PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET
Hidetake Hirayama
a
, Mizuki Tomita
b
, Keitarou Hara
b a
Graduate School of Tokyo University of Information Sciences, 4-1Onaridai, Wakaba-ku, Chiba, 265-8501 Japan –
g15006hhedu.tuis.ac.jp
b
Tokyo University of Information Sciences, 4-1Onaridai, Wakaba-ku, Chiba, 265-8501 Japan
Commission VIII, WG VIII8 KEY WORDS: MODIS, Beech, Prediction modelling, Climate change
ABSTRACT:
The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from TerraMODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are
the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the
dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month TMC ，
warmth index WI ，
winter precipitation PRW and summer precipitation PRS: and five geophysical variables; topography TOPO, surface geology GEOL, soil SOIL, slope aspect ASP, and inclination INCL; were adopted as independent variables. Previous vegetation
distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that
use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.
1. INTRODUCTION
According to predictions by the fourth assessment report of IPCC, it is believed that the world average surface temperature
will rise by about 1.8 to 4.0 °C by the end of 21
st
century IPCC, 2007. Impacts of this climate change include not only rising
temperatures, but shifts in the amount and timing of precipitation as well. Climate change can thus be expected to have a severe
impact on various types of ecosystem IPCC, 2007.
As one study on the effect of climate change on ecosystems, future changes in forest distribution areas were predicted by
modelling a potential distribution area of natural forests, especially Japanese beech forests Matsui et al., 2004, 2009.
These results of these predictions can be utilized for planning countermeasures against future ecological changes and their
associated impacts.
These predictive studies, however, relied on vegetation maps produced by the Ministry of Environment MOE National
Survey on the Natural Environment. The MOE maps are based on detailed field surveys supplemented by interpretation of
vegetation based on aerial and satellite photographs. This process is cumbersome and time-consuming, and difficult to implement
in some areas of rough terrain or accumulated snow. As a result the MOE maps are often incomplete or out of date. Even in case
of the relatively new dataset produced by the fifth National Survey on the Natural Environment, the corresponding survey
period was from 1994 to 1998, a gap of about 20 years from today.
In recent years, classification of remotely sensed data, which is able to cover a wide area and can be easily updated, has been
identified as a means of coping with the limitations of conventional mapping processes Hioki, 2007. At Tokyo
University of Information Science TUIS, nation-wide land cover classification maps hereinafter referred to as
“present
Corresponding author
vegetation map ” have been developed using MODIS data, which
is available frequently and over a wide geographic area. In the current study, the present vegetation map is employed to analyse
future vegetation shifts based on forecasted climate change. The results are compared with those derived from existing vegetation
maps, with the goal of demonstrating the effectiveness of MODIS data as a tool for wide scale prediction of future vegetation
changes.
2. DATA AND METHODS
